Overcoming differential tumor penetration of BRAF inhibitors using computationally guided combination therapy

BRAF-targeted kinase inhibitors (KIs) are used to treat malignancies including BRAF-mutant non–small cell lung cancer, colorectal cancer, anaplastic thyroid cancer, and, most prominently, melanoma. However, KI selection criteria in patients remain unclear, as are pharmacokinetic/pharmacodynamic (PK/PD) mechanisms that may limit context-dependent efficacy and differentiate related drugs. To address this issue, we imaged mouse models of BRAF-mutant cancers, fluorescent KI tracers, and unlabeled drug to calibrate in silico spatial PK/PD models. Results indicated that drug lipophilicity, plasma clearance, faster target dissociation, and, in particular, high albumin binding could limit dabrafenib action in visceral metastases compared to other KIs. This correlated with retrospective clinical observations. Computational modeling identified a timed strategy for combining dabrafenib and encorafenib to better sustain BRAF inhibition, which showed enhanced efficacy in mice. This study thus offers principles of spatial drug action that may help guide drug development, KI selection, and combination.

1x PBS, followed by fixation with 4% PFA. The collagen content present in each insert was then measured using a Sirius Red/Fast Green Collagen Staining Kit (Chondrex Inc). Collagen content was quantified, after liquid extraction, by measuring the absorbance at 540 nm, and corrected for non-collagen content by absorbance measured at 605 nm (84). Each condition was repeated 4 times.
Mathematical modelling of BRAFi tumor interstitial transport. A multicompartmental model was used to understand BRAFi PK/PD, with the assumption that only non-protein bound drug within the tissue compartment can interact with BRAF. Schematic of the model is depicted in  Table S2. Input parameter values for dab-SiR, parent dabrafenib, and encorafenib are shown in Tables S3-4. Partial differential equations were solved using method of lines technique (85), as implemented in Matlab.
Modeling of visceral metastases as seen on imaging (Fig. 5A, 8) was depicted as a spherical avascular lesion using the same equations described in Table S2 but adjusted to account for spherical geometry. For simplicity, combination drug modeling (Fig. 8) assumed independence except for competitive on-target BRAF binding. For directly comparing dabrafenib and encorafenib, kon rate of the former was adjusted 7.5x higher to match relative biochemical binding affinity between the two drugs reported in Delord et al., 2017 (2).

Indirect comparison of clinical efficacy.
Each of the three FDA-approved BRAFi/MEKi combinations for V600-mutant melanoma was compared to vemurafenib single-agent therapy, allowing for a cross-trial, indirect efficacy comparison. In the COMBI-v trial, D/T was compared to vemurafenib plus placebo, and was associated with an ORR of 64% compared to 51% in patients treated with single-agent vemurafenib. In the co-BRIM study, vemurafenib / cobimetinib (V/C) was compared with vemurafenib plus placebo. The ORR was 68% for the combination versus 48% for single-agent vemurafenib. The COLUMBUS trial compared E/B to single-agent vemurafenib (as well as to single-agent encorafenib). The ORR of combination therapy was 64% compared to 41% with single-agent vemurafenib in centralized review of imaging. It is important to note that the patient cohorts were well balanced and similar across trials ( Figure   S2D), although patients who received E/B were less likely to have an elevated LDH (29%) than patients who received D/T (34%) or V/C (43%). The method of Bucher et al. was applied in which the indirect pairwise comparisons of D/T, V/C, or E/B are adjusted according to the results of the direct comparisons of each with the common control arm, vemurafenib 39 . Indirect efficacy comparisons of PFS and OS are expressed as relative hazard ratios (HR); indirect comparisons of ORR are expressed as relative risk ratios. All are accompanied by 95% confidence intervals. Inference is based on chi-squared tests.
Crystal structure visualization. Crystal structure visualizations of inhibitor-target complexes were visualized using the standard web browser viewer of rcsb.org (accessed 12-2019) and author-assigned assemblies for 5CSW, "B-RAF in complex with Dabrafenib" (86).

Statistics.
Image quantification was performed using Fiji / ImageJ (70) or CellProfiler v3.1.9 (in vitro ERK-KTR imaging) (71). Data analysis was performed using Matlab R2017a (Mathworks, Natick, MA) and PRISM v8 (Graphpad, San Diego, CA). Log-linear analysis was performed as described (72), accessed 08-2020. Of note, this method does not allow for intra-patient covariation in lesion response to be accounted for, and lesions in the different individual anatomical sites (skin, lymph nodes, abdomen, etc.) were considered independent. This simplification motivated additional examination of intra-patient correlation in Fig. S14 using Fisher's exact test (see below). Statistical tests are indicated in figure captions and were twotailed with α = 0.05 p-value threshold.
Broad Repurposing Library data (PRISM Repurposing Secondary Screen 19Q4) was analyzed by calculating an AUC measurement by averaging reported viability values across all doses.
Since same doses were used across drugs, and doses were evenly spaced in log-space (4-fold dilution), the AUC measurement is an area under the log-linear curve, normalized to a value of 1 if all viability measurements equal 1. Analysis was also performed using normalized integral procedures provided by depmap.org (21), yielding similar results (Fig. S15). Retrospective review was performed of clinical imaging reports obtained before and during treatment. Radiologic imaging and associated reports interpreted as part of standard clinical practice were used to identify anatomical tumor location and response on a tumor-by-tumor basis. Baseline scan was defined as imaging performed prior to BRAFi/MEKi treatment onset.

Retrospective Clinical
Imaging obtained throughout the BRAFi/MEKi treatment course was also analyzed. Radiologic response assessments often use Response Evaluation Criteria In Solid Tumors (RECIST 1.1) or similar, which simplify intrapatient heterogeneity of multifocal disease by pooling and summarizing measurements across organs (87). Here, RECIST 1.1 definitions for measurable disease were used to identify lesions, and RECIST 1.1 definitions for response (including 30% decrease in diameter denoting the threshold for partial response in tumors) were also used, however here sums of diameters were not computed across all organ sites as indicated in RECIST. 2-[18F]fluoro-2-deoxy-D-glucose (FDG) PET/CT (using the CT for lesion measurement), computed tomography of the chest, abdomen and/or pelvis, and magnetic resonance imaging of the brain were analyzed. Lesions were identified on cross-sectional imaging for the following organ sites: skin, lymph nodes, lung, along the pleura, visceral abdomen and muscle. The long axis diameter of up to 2 target lesions in each organ (or in the case of lymph nodes, the short axis) were summed in the baseline scan. The same lesions were compared in follow-up studies. In most cases follow-up reports made size comparison with previous studies (and corresponding re-measurement of lesion diameter), and these data were We hypothesized that BRAFi-naïve patients receiving D/T would show tumor-site bias for response compared to patients receiving E/B that had already received prior D/T. Across both treatment regimens, 40-55% of lesions responded to therapy depending on their anatomical tumor site, with the exception of tumors in the bone and brain, of which 11% and 33% responded, respectively. Due to limited sample size, lung and pleural lesions were pooled, skin and nodal lesions were pooled, and abdominal (primarily splenic and liver) lesions were pooled with intramuscular lesions. Bone and brain lesions were excluded due to limited sample sizes and biological distinction.
Intra-patient correlation in lesion response across tissue sites was found between skin and lymph node lesions, such that if skin lesions responded, lymph node lesions in the same patient were more likely to also respond (odds ratio, 12.8, 1.2-160 95% CI, n = 22 patients). However, less correlation was found when comparing response in either skin or lymph node lesions versus response in either abdominal or muscle lesions ( Fig. S14; odds ratio, 2.3, 0.6-11 95% CI, n = 30 patients). Despite the limited sample sizes and potential tumor genetic confounders, this subset analysis further suggests response to BRAFi/MEKi depends on the tissue context. Mass Spectrometry Imaging. YUMMER1.7 liver tumors were generated as in other experiments using intrasplenic injection, and subcutaneous tumors were also inoculated, with treatment performed approximately 2 weeks later. Intracranial PDX-melanoma metastases were generated as previously described (88)  Quantification of p-ERKT202/Y204 was performed using Columbus imaging software (PerkinElmer). Cells contours were identified by a threshold on the CellMask™ Blue Stain Cells. Single-cell pERK intensity was quantified as the mean signal with the cell segmentation boundary. Mean intensity per well was calculated from all detected single-cells in that well, and mean overall p-ERK intensities and standard deviation per conditions were calculated from three well replicates using custom MATLAB 2017a code. pERK intensities within each 96-well plate were normalized by dividing the signal from untreated control cells.
Companion in-vitro microscopy assays were similarly performed using A375 and ES2 cells expressing the ERK-KTR. In vitro ERK-KTR imaging was performed by seeding roughly 20,000 cells per well in an optical-bottom 96-well plate (Ibidi, Germany), and treated as above with described concentrations of dabrafenib, dab-SiR and encorafenib. Cells were subsequently fixed with 4% PFA, followed by nuclear staining with 1 μg mL -1 DAPI, and imaging with a modified Olympus BX63 inverted microscopy system. KTR readouts were interpreted from images acquired with the same acquisition settings, background-corrected before calculating C/N ratios for individual tumor cells.
Weight and tumor size were monitored daily. Independent sets of caliper measurements were made by two researchers, which were averaged to calculate volume according to the formula V = 4/3 π (0.5 d) 3 , as in prior studies (89). To better control for differences in initial tumor size, tumor growth was calculated as a fraction of initial size. Minimum tumor size was set to 15 mm 3 across all groups during the fold-change calculation to control for caliper quantitation/detection limits. Tumors >10 mm in diameter, body condition score 2 or less, and tumor ulceration were all humane endpoint criteria. The last available caliper measurement was used in statistical calculation (Fig. 8C) if animals were sacrificed earlier due to humane endpoint consideration.

Chemical Synthesis and Characterization.
All reagents, unless otherwise noted, were purchased from commercial sources without further purification. Chloroform, methanol and THF were dried using PURESOLV-columns (Inert

Fig. S4. Activity of dab-SiR. (A)
The binding affinity (IC50) of dab-SiR to purified recombinant V600E-mutant BRAF was determined and compared to binding of the parent dabrafenib (data are means ± s.e., n = 3). (B) Corresponding to data as in Fig. 2I, single-cell dab-SiR uptake was correlated with C/N activity readouts for ERK and JNK (Spearman rank correlation calculated across n = 30 cells per tumor, and then averaged across 3 tumors; two-tailed, one-sample wilcoxon test, means ± s.e.m). (C) Single-cells were classified by dab-SiR uptake and were compared over time for JNK activity (two-way ANOVA, n = 20 total cells per tumor, across n = 3 tumors).

Fig. S5. Representative dab-SiR imaging and radial concentration profiles.
Representative confocal microscopy and quantification of dab-SiR concentration as a function of radial distance from the tumor edge are shown. Thick line and shading denote means ± s.d. across n ≥ 2 tumors per model. In some instances, data are reshown from main figures (e.g. Fig. 3A for A375 model). Fig. 3, diameters of tumors treated with dab-SiR are reported for matched subcutaneous and orthotopic models, showing no significant difference in size between the two at the time of imaging (P = 0.50, twoway ANOVA, n = 49 total tumors).

Fig. S7. Imaging of vasculature in subcutaneous and orthotopic tumor models. (A)
Fluorescent lectin was injected prior to tumor excision to label tumor vasculature. Representative confocal microscopy and quantification of integrated lectin intensity as a function of radial distance from the tumor edge are shown. Thick line and shading denote means ± s.d. across n ≥ 2 tumors per model, with exception of YUMMER1.7 liver, measured once but over multiple line-profiles.

Fig. S8. In vitro dabrafenib cytotoxicity in YUMMER mouse melanoma cells.
In a 96-well format, roughly 5,000 YUMMER1.7 cells per well were treated with a dose response of dabrafenib for 72 hr, and cell count was assessed by fluorescence microscopy immediately after DAPI counterstaining to label nuclei. The absolute IC50 and 95% confidence intervals are reported.    Fig. 6D (which shows 180 min timepoint). A transwell cell-culture insert system in a similar configuration as described in Fig. 6A was used to assess albumin transport across a barrier of activated fibroblasts. AlexFluor 647-albumin transported into the lower well compartment was compared to controls, without an insert, for transwells prepared with and without activated mouse tumor-associated fibroblasts seeded at high (70 000 cells/well) and medium (35 000 cells/well) concentrations (mean ± s.e., analysis performed with a two-way ANOVA with repeated measures). (B) Collagen absorbance signal was quantified after Sirius red staining and liquid extraction (mean ± s.e., one-way ANOVA).   Fig. 8A was performed for multiple single and combination treatment regimens. (B) Male C57Bl/6 mice were treated with BRAFi, and individual tumor growth measurements on day 9 were quantified for their initial response variability across the cohort (coefficient of variation, C.V. = std. dev. / mean). (C) Body weight was measured daily and no subjects lost >15% body weight. Data are means ± s.e.m. across n=19 total mice. (D) Indirect comparison of adverse events with at least 20% incidence in pivotal E/B and D/T trials among patients with advanced melanoma (see Fig. S1D). Data are means ± 95% C.I. (E-G) Nu/nu mice bearing hepatic YUMMER1.7 melanoma tumors were treated daily by oral gavage, body weight was monitored daily (F), and survival was monitored following guidelines for humane experimental endpoints (n=15 total mice across 4 groups; group colors match F). Images of excised livers, and subject-matched abdominal tumor mass in a dabrafenibtreated mouse, at time of sacrifice show relatively less tumor burden following 15 mg/kg encorafenib-treatment (E).

Fig. S14. Analysis of intrapatient correlation in lesion response. From patients receiving
BRAFi/MEKi combination therapy (described in Fig. 1A-B), an analysis was performed to examine correlation in lesion responses across tissue sites. Responses (defined here as lesions shrinking >30% at the indicated anatomical site) were tabulated after first stratifying based on whether responses (as similarly defined) were noted at a separate indicated tumor site (the skin [in first comparison], or either skin and lymph node [second/third comparisons]). Odds ratios were then calculated (mean ± 95% CI) in comparing rates of lesion response depending on whether or not response was noted at the separate tumor site in the same patient and same course of BRAFi/MEKi. Data in table are patient counts, p-values in odds-ratios are from Fisher's exact test.
Fig. S15. Broad Repurposing Library alternative analysis. Analyses were performed as in Fig. S3, but using the depmap.org AUC calculation method of the normalized integral (21) rather than the normalized integral under the log-linear curve (see Methods). Despite differences in calculation, the AUC ratio trends were consistent (Fig. S3). Data are individual cell lines shown after standard depmap.org inclusion/filtering criteria (two-tailed rank-sum).   Fig. 4A with appropriate values pertinent for Dab-SiR modelling. These are presented alongside references from which the values were taken. Table S4. Input model parameter values. Data correspond to the different drugs and doseadministration pathways used in this study. These are presented alongside references from which the values were taken. Table S5. Analysis of simulated dabrafenib behavior in response to altered model parameters. Corresponds to Fig. 4F-H. Model rate constants were adjusted as shown, and compared to simulations of parent dabrafenib under conditions depicted in Fig. 4C. Table S6. Highly plasma protein bound oncology drugs. Drugs exhibiting ≥99% plasma protein binding and approved by the US FDA for indications in oncology are reported, along with their calculated water : octanol partition coefficient (cLogP) as a metric of lipophilicity (54).